Low-frequency ultrasound for pulmonary hypertension therapy.

BACKGROUND: Currently, there are no reliable clinical tools that allow non-invasive therapeutic support for patients with pulmonary arterial hypertension. This study aims to propose a low-frequency ultrasound device for pulmonary hypertension therapy and to demonstrate its potential. METHODS: A novel low-frequency ultrasound transducer has been developed. Due to its structural properties, it is excited by higher vibrational modes, which generate a signal capable of deeply penetrating biological tissues. A methodology for the artificial induction of pulmonary hypertension in sheep and for the assessment of lung physiological parameters such as blood oxygen concentration, pulse rate, and pulmonary blood pressure has been proposed. RESULTS: The results showed that exposure of the lungs to low-frequency ultrasound changed physiological parameters such as blood oxygen concentration, pulse rate and blood pressure. These parameters are most closely related to indicators of pulmonary hypertension (PH). The ultrasound exposure increased blood oxygen concentration over a 7-min period, while pulse rate and pulmonary blood pressure decreased over the same period. In anaesthetised sheep exposed to low-frequency ultrasound, a 10% increase in SpO2, a 10% decrease in pulse rate and an approximate 13% decrease in blood pressure were observed within 7 min. CONCLUSIONS: The research findings demonstrate the therapeutic efficiency of low-frequency ultrasound on hypertensive lungs, while also revealing insights into the physiological aspects of gas exchange within the pulmonary system.

Development of a Low-Frequency Piezoelectric Ultrasonic Transducer for Biological Tissue Sonication.

The safety of ultrasound exposure is very important for a patient's well-being. High-frequency (1-10 MHz) ultrasound waves are highly absorbed by biological tissue and have limited therapeutic effects on internal organs. This article presents the results of the development and application of a low-frequency (20-100 kHz) ultrasonic transducer for sonication of biological tissues. Using the methodology of digital twins, consisting of virtual and physical twins, an ultrasonic transducer has been developed that emits a focused ultrasound signal that penetrates into deeper biological tissues. For this purpose, the ring-shaped end surface of this transducer is excited not only by the main longitudinal vibrational mode, which is typical of the flat end surface transducers used to date, but also by higher mode radial vibrations. The virtual twin simulation shows that the acoustic signal emitted by the ring-shaped transducer, which is excited by a higher vibrational mode, is concentrated into a narrower and more precise acoustic wave that penetrates deeper into the biological tissue and affects only the part of the body to be treated, but not the whole body.

Investigation of the Robotized Incremental Metal-Sheet Forming Process with Ultrasonic Excitation.

During the single-point incremental forming (SPIF) process, a sheet is formed by a locally acting stress field on the surface consisting of a normal and shear component that is strongly affected by friction of the dragging forming tool. SPIF is usually performed under well-lubricated conditions in order to reduce friction. Instead of lubricating the contact surface of the sheet metal, we propose an innovative, environmentally friendly method to reduce the coefficient of friction by ultrasonic excitation of the metal sheet. By evaluating the tool-workpiece interaction process as non-linear due to large deformations in the metal sheet, the finite element method (FEM) allows for a virtual evaluation of the deformation and piercing parameters of the SPIF process in order to determine destructive loads.

A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes.

There are many tool condition monitoring solutions that use a variety of sensors. This paper presents a self-powering wireless sensor node for shank-type rotating tools and a method for real-time end mill wear monitoring. The novelty of the developed and patented sensor node is that the longitudinal oscillations, which directly affect the intensity of the energy harvesting, are significantly intensified due to the helical grooves cut onto the conical surface of the tool holder horn. A wireless transmission of electrical impulses from the capacitor is proposed, where the collected electrical energy is charged and discharged when a defined potential is reached. The frequency of the discharge pulses is directly proportional to the wear level of the tool and, at the same time, to the surface roughness of the workpiece. By employing these measures, we investigate the support vector machine (SVM) approach for wear level prediction.

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force.

This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.

Peculiarities of the third natural frequency vibrations of a cantilever for the improvement of energy harvesting.

This paper focuses on several aspects extending the dynamical efficiency of a cantilever beam vibrating in the third mode. A few ways of producing this mode stimulation, namely vibro-impact or forced excitation, as well as its application for energy harvesting devices are proposed. The paper presents numerical and experimental analyses of novel structural dynamics effects along with an optimal configuration of the cantilever beam. The peculiarities of a cantilever beam vibrating in the third mode are related to the significant increase of the level of deformations capable of extracting significant additional amounts of energy compared to the conventional harvester vibrating in the first mode. Two types of a piezoelectric vibrating energy harvester (PVEH) prototype are analysed in this paper: the first one without electrode segmentation, while the second is segmented using electrode segmentation at the strain nodes of the third vibration mode to achieve effective operation at the third resonant frequency. The results of this research revealed that the voltage generated by any segment of the segmented PVEH prototype excited at the third resonant frequency demonstrated a 3.4-4.8-fold increase in comparison with the non-segmented prototype. Simultaneously, the efficiency of the energy harvester prototype also increased at lower resonant frequencies from 16% to 90%. The insights presented in the paper may serve for the development and fabrication of advanced piezoelectric energy harvesters which would be able to generate a considerably increased amount of electrical energy independently of the frequency of kinematical excitation.